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---
license: mit
base_model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sem_eval_2024_task_2
type: sem_eval_2024_task_2
config: sem_eval_2024_task_2_source
split: validation
args: sem_eval_2024_task_2_source
metrics:
- name: Accuracy
type: accuracy
value: 0.715
- name: Precision
type: precision
value: 0.7186959617536364
- name: Recall
type: recall
value: 0.7150000000000001
- name: F1
type: f1
value: 0.7137907659862921
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results2
This model is a fine-tuned version of [MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7766
- Accuracy: 0.715
- Precision: 0.7187
- Recall: 0.7150
- F1: 0.7138
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6998 | 1.0 | 107 | 0.6713 | 0.6 | 0.6214 | 0.6000 | 0.5815 |
| 0.7015 | 2.0 | 214 | 0.6502 | 0.68 | 0.7143 | 0.6800 | 0.6667 |
| 0.6755 | 3.0 | 321 | 0.6740 | 0.53 | 0.6579 | 0.53 | 0.4107 |
| 0.6605 | 4.0 | 428 | 0.6061 | 0.64 | 0.6502 | 0.64 | 0.6338 |
| 0.5918 | 5.0 | 535 | 0.5675 | 0.695 | 0.7023 | 0.6950 | 0.6922 |
| 0.5717 | 6.0 | 642 | 0.5945 | 0.685 | 0.6953 | 0.685 | 0.6808 |
| 0.4655 | 7.0 | 749 | 0.5644 | 0.68 | 0.6801 | 0.6800 | 0.6800 |
| 0.3407 | 8.0 | 856 | 0.7529 | 0.7 | 0.7029 | 0.7 | 0.6989 |
| 0.3539 | 9.0 | 963 | 0.7211 | 0.69 | 0.6901 | 0.69 | 0.6900 |
| 0.2695 | 10.0 | 1070 | 0.7760 | 0.685 | 0.6905 | 0.685 | 0.6827 |
| 0.1666 | 11.0 | 1177 | 1.1053 | 0.71 | 0.7188 | 0.71 | 0.7071 |
| 0.1648 | 12.0 | 1284 | 1.1662 | 0.72 | 0.7258 | 0.72 | 0.7182 |
| 0.1229 | 13.0 | 1391 | 1.2760 | 0.735 | 0.7438 | 0.735 | 0.7326 |
| 0.0737 | 14.0 | 1498 | 1.5943 | 0.7 | 0.7029 | 0.7 | 0.6989 |
| 0.1196 | 15.0 | 1605 | 1.5407 | 0.705 | 0.7085 | 0.7050 | 0.7037 |
| 0.0389 | 16.0 | 1712 | 1.6411 | 0.69 | 0.7016 | 0.69 | 0.6855 |
| 0.0199 | 17.0 | 1819 | 1.7139 | 0.685 | 0.6919 | 0.685 | 0.6821 |
| 0.0453 | 18.0 | 1926 | 1.6549 | 0.71 | 0.7121 | 0.71 | 0.7093 |
| 0.0536 | 19.0 | 2033 | 1.7612 | 0.71 | 0.7142 | 0.71 | 0.7086 |
| 0.0035 | 20.0 | 2140 | 1.7766 | 0.715 | 0.7187 | 0.7150 | 0.7138 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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